Tirzepatide 10 and 15 mg compared with semaglutide 2.4 mg for the treatment of obesity: An indirect treatment comparison
Bibliographic record
Abstract
AIM: To compare the efficacy of tirzepatide 10 and 15 mg with semaglutide 2.4 mg using an indirect treatment comparison. MATERIALS AND METHODS: Using SURMOUNT-1 and STEP 1 trial data, mean percentage change in body weight from baseline and odds ratio (OR) of achieving 5% or greater weight loss were compared between tirzepatide 10 and 15 mg at week 72 and semaglutide 2.4 mg at week 68 using matching-adjusted indirect comparison of the efficacy estimand. Sensitivity analyses were completed using different methods, including the Bucher method, also using different estimands and/or time points. RESULTS: Greater reductions in percentage change in body weight were observed with tirzepatide 10 and 15 mg versus semaglutide 2.4 mg (tirzepatide 10 mg mean difference: -4.67% [95% CI -5.91%, -3.43%]; tirzepatide 15 mg mean difference: -5.92% [95% CI -7.16%, -4.68%]; both P < .001). Similarly, more participants achieved 5% or greater weight loss with tirzepatide 10 mg (OR 2.61 [95% CI 1.48, 4.57]; P < .001) and 15 mg (OR 2.75 [95% CI 1.57, 4.81]; P < .001) compared with semaglutide 2.4 mg. All sensitivity analyses were consistent, except for an OR of achieving 5% or greater weight loss with tirzepatide 10 mg using the Bucher method to analyse the treatment regimen estimand (P = .074). CONCLUSIONS: Currently there are no direct comparisons of tirzepatide and semaglutide for weight management. Using the matching-adjusted indirect treatment comparison method to compare the efficacy of tirzepatide and semaglutide for chronic weight management, this analysis showed greater weight loss with tirzepatide 10 and 15 mg versus semaglutide 2.4 mg.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".